A recent practitioner commentary offers a confident assessment of the current state of large language models (LLMs) in legal practice, arguing that the primary barriers to adoption are no longer questions of intelligence or reliability but rather issues of infrastructure and workflow integration. Writing from the perspective of a lawyer who uses advanced models daily, the author contends that modern systems have already reached a level of practical competence sufficient for much of routine legal work, and that the profession’s hesitation reflects outdated assumptions about hallucinations and model limitations.
Central to the argument is the claim that hallucinations, once the dominant concern surrounding generative AI, have largely receded as a meaningful obstacle. According to the author’s experience, newer models rarely produce fabricated information, and overall error rates compare favorably with those of competent junior associates. This view reflects a broader shift in perception: rather than treating LLMs as experimental tools requiring constant skepticism, the author frames them as increasingly dependable collaborators capable of supporting substantive legal tasks.
The post also challenges prevailing narratives about the intellectual difficulty of legal work. While acknowledging that certain cases demand deep expertise, the author suggests that the majority of legal tasks rely on skills such as careful reasoning, synthesis of precedent, structured writing, and research , areas where modern LLMs already excel. By reframing legal practice as process-driven rather than exclusively intellectually rarefied, the commentary positions AI as well aligned with the day-to-day realities of the profession.
From this perspective, the real bottlenecks to widespread adoption lie elsewhere. The author emphasizes the need for improved connectivity to legal databases, better user interfaces, and robust deployment “harnesses” that integrate AI into existing workflows. In other words, the limiting factor is no longer model capability but the surrounding ecosystem required to make these tools usable at scale within law firms and legal departments. Economic incentives reinforce this view because legal services generate significant value. The author anticipates rapid progress in solving integration challenges, particularly as the cost of running advanced reasoning models continues to fall.
Looking ahead, the post offers a cautiously optimistic forecast for the next 12 to 18 months. Even under conservative assumptions, including limited breakthroughs in agentic AI or fundamental intelligence gains, the author expects meaningful advances driven by faster, cheaper versions of today’s top-tier models. Should more ambitious developments occur, such as major improvements in autonomy or learning capabilities, the pace of change could accelerate further.
Taken together, the commentary reflects a broader strategic theme emerging within parts of the legal profession: the debate may be shifting from whether LLMs are intelligent enough to assist lawyers toward how quickly institutions can build the infrastructure needed to deploy them effectively. For legal information professionals and law librarians in particular, this perspective underscores a growing emphasis on information architecture, system integration, and governance, areas likely to shape how AI ultimately transforms legal research and practice.